Privacy-Preserving Classification of Personal Data with Fully Homomorphic Encryption: An Application to High-Quality Ionospheric Data Prediction

2020 ◽  
pp. 437-446
Author(s):  
Zheng Li ◽  
Maohua Sun
Keyword(s):  
Homomorphic Encryption ◽  
Personal Data ◽  
Privacy Preserving ◽  
Fully Homomorphic Encryption ◽  
High Quality ◽  
Data Prediction

scholarly journals Accelerating privacy-preserving momentum federated learning for industrial cyber-physical systems

2021 ◽  
Author(s):  
Linlin Zhang ◽  
Zehui Zhang ◽  
Cong Guan
Keyword(s):  
Convergence Rate ◽  
Performance Improvement ◽  
Gradient Descent ◽  
Homomorphic Encryption ◽  
Cyber Physical Systems ◽  
Privacy Preserving ◽  
Fully Homomorphic Encryption ◽  
Physical Systems ◽  
Cloud Server ◽  
And Performance

AbstractFederated learning (FL) is a distributed learning approach, which allows the distributed computing nodes to collaboratively develop a global model while keeping their data locally. However, the issues of privacy-preserving and performance improvement hinder the applications of the FL in the industrial cyber-physical systems (ICPSs). In this work, we propose a privacy-preserving momentum FL approach, named PMFL, which uses the momentum term to accelerate the model convergence rate during the training process. Furthermore, a fully homomorphic encryption scheme CKKS is adopted to encrypt the gradient parameters of the industrial agents’ models for preserving their local privacy information. In particular, the cloud server calculates the global encrypted momentum term by utilizing the encrypted gradients based on the momentum gradient descent optimization algorithm (MGD). The performance of the proposed PMFL is evaluated on two common deep learning datasets, i.e., MNIST and Fashion-MNIST. Theoretical analysis and experiment results confirm that the proposed approach can improve the convergence rate while preserving the privacy information of the industrial agents.

scholarly journals Efficient Privacy-Preserving Fingerprint-Based Authentication System Using Fully Homomorphic Encryption

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Taeyun Kim ◽  
Yongwoo Oh ◽  
Hyoungshick Kim
Keyword(s):  
Hash Function ◽  
Web Applications ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Fingerprint Matching ◽  
Fully Homomorphic Encryption ◽  
Matching Process ◽  
Authentication System ◽  
Fingerprint Database ◽  
Fingerprint Data

To help smartphone users protect their phone, fingerprint-based authentication systems (e.g., Apple’s Touch ID) have increasingly become popular in smartphones. In web applications, however, fingerprint-based authentication is still rarely used. One of the most serious concerns is the lack of technology for securely storing fingerprint data used for authentication. Because scanned fingerprint data are not exactly the same each time, the use of a traditional cryptographic hash function (e.g., SHA-256) is infeasible to protect raw fingerprint data. In this paper, we present an efficient privacy-preserving fingerprint authentication system using a fully homomorphic encryption scheme in which fingerprint data are always stored and processed in an encrypted form. We implement a fully working fingerprint authentication system with a fingerprint database (containing 4,000 samples) using the Fast Fully Homomorphic Encryption over the Torus (TFHE) library. The proposed system can perform the fingerprint matching process within about 166 seconds (±0.564 seconds) on average.

PRIVACY PRESERVING CLASSIFICATION OVER ENCRYPTED DATA USING FULLY HOMOMORPHIC ENCRYPTION TECHNIQUE

2018 ◽  
Vol 6 (2) ◽  
pp. 36
Author(s):  
MONDAY JUBRIN ABDULLAHI ◽  
ONOMZA WAZIRI VICTOR ◽  
BASHIR ABDULLAHI MUHAMMAD ◽  
ISMAILA IDRIS ◽  
◽  
...  
Keyword(s):  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Fully Homomorphic Encryption ◽  
Encrypted Data
Sign in / Sign up

Export Citation Format

Share Document